Forecasting quantile and value-at-risk levels for commodity prices is methodologically challenging because of the distinctive stochastic properties of the price density functions, volatility clustering and the importance of exogenous factors. Despite this, accurate risk measures have considerable value in trading and risk management with the topic being actively researched for better techniques. We approach the problem by using a multifactor, dynamic, quantile regression formulation, extended to include GARCH properties, and applied to both in-sample estimation and out-of-sample forecasting of traded electricity prices. This captures the specification effects of mean reversion, spikes, time varying volatility and demonstrates how the prices of gas, coal and carbon, forecasts of demand and reserve margin in addition to price volatility influence the electricity price quantiles. We show how the price coefficients for these factors vary substantially across the quantiles and offer a new, useful synthesis of GARCH effects within quantile regression. We also show that a linear quantile regression model outperforms skewed GARCH-t and CAViaR models, as specified on the shocks to conditional expectations, regarding the accuracy of out-of-sample forecasts of value-at-risk.